TUI University College of Business Administration

TUI University College of Business Administration

BALANCING DECISION SPEED AND DECISION QUALITY: ASSESSING THE IMPACT OF BUSINESS INTELLIGENCE

SYSTEMS IN HIGH VELOCITY ENVIRONMENTS

Criston W Cox JrPhD Candidate

Dissertation CommitteeDr. Yufeng Tu

Dr. Yajiong XueDr. William Kemple

Purpose of ResearchPurpose of Research

• The purpose of this research effort is to determine if:

• the output of the BI System sufficiently balances information quality, quantity, and availability

• delivers the right information, to the right people, at the right time

• enabling quality decisions in high velocity environments.

Relevant Literature and flow to the DVRelevant Literature and flow to the DV

High Velocity Environment Faster decision = better performance

Type of information most needed in high velocity environments is real time information. This need drives

the decision to implement a real time Business Intelligence System

Enough info to make decision without information overload. Measured

by Number of Alternatives

Jacoby, Russo, Malhotra, Gallupe, et al.

(accuracy) Haubl and Trifts, 2000; Sharda, Barr, and McDonald, 1988;

BI System Usage Frequency and Length of Use

depends on three primary effects of information (which affect user satisfaction)

Eisenhardt and Bourgeois, 1989 Bogner and Barr, 2000; Judge and Miller, 1995, Baum and Wally, 2003

Effectiveness of the BI System to provide timely, accurate , and

relevant information (R3) / Impact on OODA Loop

Bryant, 2006; Negash, 2004, Nicolas, 2004; Stalk and Hout, 1990

Impact on Decision QualityGreater access to needed InformationLeidner and Elam (1995)

Reduced cognitive effort(Todd and Benbaset, 2000)

Information Quality

Information Quantity

Information Availability

Sawy and Majchrzak, 2004; Eisenhardt and Bourgeois, 1989

Delone and McLean, 1992; Leidner and Elam, 1995; Huber, 1990; Jones and Straub, 2006

multicolinearity Oreilly, 1982 found significant associations among both information

quality and availability of information sources, and the frequency of their use.

Research QuestionsResearch Questions

Research Question 1:

Do BI Systems enable faster and better decisions in High Velocity Environments, or are decision speed and decision quality inversely related?

Eisenhardt and Bourgeois, 1989; Bogner and Barr, 2000; Judge and Miller, 1995; Baum and Wally, 2003.

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Research Questions (continued)Research Questions (continued)

Research Question 2:

What is the relationship between usage of a BI system and the quality of decisions made in a High Velocity Environments?

(Delone and McLean, 2005; Burton-Jones and Straub, 2006; Baroudi, Olson, and Ives, 1986; Straub, Limayem, and Karahanna-Evaristo, 1995).

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Research Questions (continued)Research Questions (continued)

Research Question 3:

What affect do BI systems have on information overload in High Velocity Environments?

(Keller and Staelin, 1987; Jacoby, 1974; Malhotra, 1982; Russo, 1974).

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Conceptual ModelConceptual Model

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Variables Mapped to Survey QuestionsVariables Mapped to Survey Questions

Variable Label (Type) Code Survey Question Number

Source of Survey Item

Decision QualityDependent Variable

DcnQu 6-1112-15

Dooley and Fryxell, 1999Paul, Saunders, and Haseman, 2005

Decision Speed DcnSp 19-21 Leidner and Elam, 1995

System Usage SysUs 16a-d, 1817

Leidner & Elam, 2005Iivari, 2005

Information Overload InfoOv 31-35 O’Reilly, 1980

Information Availability InfoAv 28-30 Leidner and Elam, 1995

Information Quality InfoQu 22-27 Iivari, 2005

Research Design and MethodologyResearch Design and Methodology

• To empirically measure effect size of associations between the variables, constructs will be tested using Structural Equation Modeling (SEM).

• Survey is hosted on Zoomerang.com. Participants are solicited using Linked-In User Groups.

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Job Level

Distribution of Major BI Systems

Organization Size

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VARIABLE CRONBACH'S ALPHA

SYSTEM USAGE 0.65

INFORMATION OVERLOAD 0.73

INFORMATION QUALITY 0.89

INFORMATION AVAILABILITY 0.78

DECISION SPEED 0.75

DECISION QUALITY 0.82

Internal Consistency

SysUs

InfoQu

DcnQu

DcnSp

InfoOv

InfoAv

-.09 -.04

.58

.34

.30

.32

.42

.55

e1

1

.38

e2

1

.66

e3

.55

e4

1

.16

e5

1

-.05

.50

e6

1 1

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GFI=.72

Path Analysis

cwcox@tuiu.educwcox@tuiu.edu

Backup SlidesBackup Slides

Population and SamplePopulation and Sample

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•The population under study are those who employ Business Intelligence Systems to aid in rapid decision making in High Velocity Environments.

•Research indicates an adequate sample size for CFA based SEMs is 150 (Ding, Velicer, and Harlow (1995); Anderson and Gerbring, 1998); Muthen and Muthen, 2002).

Based on the recommendations of previous SEM research, the sample size desired for this study is 300, with a minimum acceptance of 150.

–It is estimated that a minimum of 1500 surveys must be send to yield the desired sample size of 300.

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Research ContributionsResearch Contributions

This study has both theoretical and practical implications.

• First, from the theoretical perspective, this study contributes to IS and Decision Science literature by pulling topics from each together into a cohesive set of dependent and influencing relationships. Decision theory is a mature area of research that has shaped the development of decision support systems. Understanding the value of decision support systems as they grow and evolve with technological advances is important to the continued development of information systems sciences. .

• Second, this study enhances our understanding about the value of BI Systems as a decision aid, which may prove beneficial to organizations considering adoption and investment in a BI System. The outcome of this research will extend the knowledge of BIS within the information systems community.

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Variables and HypothesesVariables and Hypotheses

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Variables Hypotheses

Decision QualityDependent Variable

Decision quality is the Dependent Variable

Decision Speed H1: When enabled with a Business Intelligence System, Decision quality is positively associated to decision speed.

System UsageIndependent Variable

H2: Higher BI system usage is positively associated to greater information availability.H3: Higher BI System Usage reduces information overload.H4: Higher BI System Usage is positively related to information quality.

Information Overload Mediating Variable

H5: BI aided groups will consider a greater number of simultaneous alternatives than non BI aided groups.H6: The number of alternatives is inversely related to the decision speed. H7: The number of alternatives is positively related to the decision quality.

Information AvailabilityMediating Variable

H8: Information availability is positively associated with decision speed.

Information QualityMediating Variable

H9: The quality of the information is positively associated with decision effectiveness.